Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

Abstract

Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study.

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

“…In these papers, the authors make use of a variety of different types of sensors. In [17], Brennan et al In the identified papers, the reasons for using the Discriminant Analysis method with sensor devices in smart buildings were equally distributed between human activity recognition/classification [16,17,62] and the detection of human behavior in the context of assisted living [33,61,66].…”

“…The authors of [66] implemented the Discriminant Analysis technique. In [33], the authors implemented a Hidden Markov Model (HMM), Viterbi path counting, and a scalable Stochastic Variational Inference (SVI)-based training algorithm, along with Generalized Discriminant Analysis. In [62], the authors made use of various methods of feature extraction (Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA)) and the new features selected by each method were subsequently used as the inputs for a Weighted Support Vector Machines (WSVM) classifier.…”

“…The performance metrics considered in the scientific papers that use the Discriminant Analysis technique integrated with sensor devices in Smart Buildings include: Accuracy [16,17,33,61,62,66]; Precision [61,62]; Recall [16,61,62] and F-measure [33,61,62]; Root-Mean-Square Error (RMSE) [17]; Coefficient of Variance (CV) [17]; Normalized Root-Mean-Square Error (NRMSE) [17]; Coefficient of Variation of the RMSD (CV) [17]; Sensitivity (Sen) [33], Specificity (Spe) [33]; and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) [33].…”

“…There is an increasing interest in the scientific literature in studies related to these topics; for example, there have been papers focusing on smart buildings [1][2][3][4][5], smart homes [6][7][8][9][10], smart hospitals [11], smart commercial buildings [12,13], sensor devices [9,[14][15][16][17], supervised machine learning models for classification purposes [1,11,[16][17][18] or for regression purposes [19][20][21][22][23], unsupervised machine learning models for clustering purposes [24][25][26], deep learning techniques [18,27,28], human activity recognition and classification with a view to assisted living [15,[29][30][31][32][33][34][35], Internet of Things (IoT) [21,[36][37][38]…”

Machine learning in mental health: A systematic scoping review of methods and applications

“…A novel approach identified for detection of conditions is the use of unstructured text, including detection of suicide ideation from counselling transcripts [10], detection of schizophrenia from written texts [11], and analysis of social media data to detect depressive symptoms [12]. ML has also been applied to wearable sensor data to assess general wellbeing [13], and to ambient, in-home sensors to detect psychiatric emergencies [14]. Finally, speech data has been used with ML to detect underlying mental states indicative of schizophrenia and depression [15], to assess the effects of drugs on mental state [16], and to classify at-risk patients of Alzheimer's disease based on speech patterns [17].…”